{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-04-11T06:43:45.330997Z",
     "start_time": "2025-04-11T06:43:30.417920Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, classification_report\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import time\n",
    "\n",
    "\n",
    "# 步骤一：数据准备与预处理\n",
    "# 加载MNIST数据集\n",
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "X, y = mnist.data, mnist.target.astype(np.int8)\n",
    "\n",
    "# 数据标准化\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 将y_train和y_test转换为numpy数组\n",
    "y_train = y_train.values\n",
    "y_test = y_test.values\n",
    "\n",
    "# 步骤二：调用ML库实现模型训练与评估\n",
    "# 感知机模型训练\n",
    "start_time = time.time()\n",
    "model = Perceptron(max_iter=1000, random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "end_time = time.time()\n",
    "training_time_sklearn = end_time - start_time\n",
    "\n",
    "# 模型预测\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# 模型评估\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision, recall, f1, _ = precision_recall_fscore_support(y_test, y_pred, average='weighted')\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "class_report = classification_report(y_test, y_pred)\n",
    "\n",
    "print(\"Sklearn Perceptron Results:\")\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1-score:\", f1)\n",
    "print(\"Training Time:\", training_time_sklearn)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"Classification Report:\\n\", class_report)\n",
    "\n",
    "# 使用热度图展示混淆矩阵\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Oranges')\n",
    "plt.title(\"Sklearn Perceptron - Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sklearn Perceptron Results:\n",
      "Accuracy: 0.8810714285714286\n",
      "Precision: 0.8809648832560745\n",
      "Recall: 0.8810714285714286\n",
      "F1-score: 0.880775650387065\n",
      "Training Time: 8.723724603652954\n",
      "Confusion Matrix:\n",
      " [[1276    2   21    7    1    7   17    4    8    0]\n",
      " [   1 1503    6   10    4   13    1   29   31    2]\n",
      " [   7   15 1231   27   15   17   14   14   33    7]\n",
      " [   9   16   34 1221    4   33   12   35   42   27]\n",
      " [   3    3   14    6 1160    6   12   10   24   57]\n",
      " [  10   13   15   59   21 1034   29    6   75   11]\n",
      " [   4   20   52    3   10   11 1277    2   14    3]\n",
      " [   8    4   15    6   13   16    0 1394    6   41]\n",
      " [  11   42   21   57    8   70   12   13 1089   34]\n",
      " [   8    3    4   24   72   26    0  101   32 1150]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.95      0.95      1343\n",
      "           1       0.93      0.94      0.93      1600\n",
      "           2       0.87      0.89      0.88      1380\n",
      "           3       0.86      0.85      0.86      1433\n",
      "           4       0.89      0.90      0.89      1295\n",
      "           5       0.84      0.81      0.83      1273\n",
      "           6       0.93      0.91      0.92      1396\n",
      "           7       0.87      0.93      0.90      1503\n",
      "           8       0.80      0.80      0.80      1357\n",
      "           9       0.86      0.81      0.84      1420\n",
      "\n",
      "    accuracy                           0.88     14000\n",
      "   macro avg       0.88      0.88      0.88     14000\n",
      "weighted avg       0.88      0.88      0.88     14000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-11T06:38:53.020343Z",
     "start_time": "2025-04-11T06:31:22.802416Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 步骤三：自主编写并实现模型算法\n",
    "class CustomPerceptron:\n",
    "    def __init__(self, learning_rate=0.01, n_iterations=1000):\n",
    "        self.learning_rate = learning_rate\n",
    "        self.n_iterations = n_iterations\n",
    "        self.weights = None\n",
    "        self.bias = None\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        n_samples, n_features = X.shape\n",
    "        self.weights = np.zeros(n_features)\n",
    "        self.bias = 0\n",
    "\n",
    "        for _ in range(self.n_iterations):\n",
    "            for idx, x_i in enumerate(X):\n",
    "                linear_output = np.dot(x_i, self.weights) + self.bias\n",
    "                y_predicted = self._activation_function(linear_output)\n",
    "\n",
    "                update = self.learning_rate * (y[idx] - y_predicted)\n",
    "                self.weights += update * x_i\n",
    "                self.bias += update\n",
    "\n",
    "    def predict(self, X):\n",
    "        linear_output = np.dot(X, self.weights) + self.bias\n",
    "        y_predicted = self._activation_function(linear_output)\n",
    "        return y_predicted\n",
    "\n",
    "    def _activation_function(self, x):\n",
    "        return np.where(x >= 0, 1, 0)\n",
    "    \n",
    "start_time = time.time()\n",
    "custom_perceptron = CustomPerceptron()\n",
    "custom_perceptron.fit(X_train, y_train)\n",
    "end_time = time.time()\n",
    "training_time_custom = end_time - start_time\n",
    "\n",
    "custom_y_pred = custom_perceptron.predict(X_test)\n",
    "\n",
    "custom_accuracy = accuracy_score(y_test, custom_y_pred)\n",
    "custom_precision, custom_recall, custom_f1, _ = precision_recall_fscore_support(y_test, custom_y_pred, average='weighted')\n",
    "custom_conf_matrix = confusion_matrix(y_test, custom_y_pred)\n",
    "custom_class_report = classification_report(y_test, custom_y_pred)\n",
    "\n",
    "print(\"Custom Perceptron Results:\")\n",
    "print(\"Accuracy:\", custom_accuracy)\n",
    "print(\"Precision:\", custom_precision)\n",
    "print(\"Recall:\", custom_recall)\n",
    "print(\"F1-score:\", custom_f1)\n",
    "print(\"Training Time:\", training_time_custom)\n",
    "print(\"Confusion Matrix:\\n\", custom_conf_matrix)\n",
    "print(\"Classification Report:\\n\", custom_class_report)\n",
    "\n"
   ],
   "id": "d3dac39da3d0a836",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Custom Perceptron Results:\n",
      "Accuracy: 0.10864285714285714\n",
      "Precision: 0.022661315055953955\n",
      "Recall: 0.10864285714285714\n",
      "F1-score: 0.03750018518115563\n",
      "Training Time: 450.1589684486389\n",
      "Confusion Matrix:\n",
      " [[1247   96    0    0    0    0    0    0    0    0]\n",
      " [1326  274    0    0    0    0    0    0    0    0]\n",
      " [1207  173    0    0    0    0    0    0    0    0]\n",
      " [1000  433    0    0    0    0    0    0    0    0]\n",
      " [  86 1209    0    0    0    0    0    0    0    0]\n",
      " [ 626  647    0    0    0    0    0    0    0    0]\n",
      " [ 601  795    0    0    0    0    0    0    0    0]\n",
      " [  30 1473    0    0    0    0    0    0    0    0]\n",
      " [ 313 1044    0    0    0    0    0    0    0    0]\n",
      " [  28 1392    0    0    0    0    0    0    0    0]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.19      0.93      0.32      1343\n",
      "           1       0.04      0.17      0.06      1600\n",
      "           2       0.00      0.00      0.00      1380\n",
      "           3       0.00      0.00      0.00      1433\n",
      "           4       0.00      0.00      0.00      1295\n",
      "           5       0.00      0.00      0.00      1273\n",
      "           6       0.00      0.00      0.00      1396\n",
      "           7       0.00      0.00      0.00      1503\n",
      "           8       0.00      0.00      0.00      1357\n",
      "           9       0.00      0.00      0.00      1420\n",
      "\n",
      "    accuracy                           0.11     14000\n",
      "   macro avg       0.02      0.11      0.04     14000\n",
      "weighted avg       0.02      0.11      0.04     14000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "D:\\python\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "D:\\python\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "D:\\python\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-11T06:44:51.435024Z",
     "start_time": "2025-04-11T06:44:51.108401Z"
    }
   },
   "cell_type": "code",
   "source": [
    " #使用热度图展示混淆矩阵\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(custom_conf_matrix, annot=True, fmt='d', cmap='Reds')\n",
    "plt.title(\"Custom Perceptron - Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()"
   ],
   "id": "44c06d783e1ab66f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-11T07:05:18.637510Z",
     "start_time": "2025-04-11T07:05:18.226957Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "\n",
    "# 步骤四：模型性能对比与分析\n",
    "results = {\n",
    "    \"Sklearn Perceptron\": {\n",
    "        \"Accuracy\": accuracy,\n",
    "        \"Precision\": precision,\n",
    "        \"Recall\": recall,\n",
    "        \"F1-score\": f1,\n",
    "        \"Training Time\": training_time_sklearn,\n",
    "        \"Confusion Matrix\": conf_matrix\n",
    "    },\n",
    "    \"Custom Perceptron\": {\n",
    "        \"Accuracy\": custom_accuracy,\n",
    "        \"Precision\": custom_precision,\n",
    "        \"Recall\": custom_recall,\n",
    "        \"F1-score\": custom_f1,\n",
    "        \"Training Time\": training_time_custom,\n",
    "        \"Confusion Matrix\": custom_conf_matrix\n",
    "    }\n",
    "}\n",
    "\n",
    "metrics = [\"Accuracy\", \"Precision\", \"Recall\", \"F1-score\", \"Training Time\"]\n",
    "metrics_df = pd.DataFrame(columns=metrics)\n",
    "\n",
    "for model_name, result in results.items():\n",
    "    metrics_df.loc[model_name] = [result[metric] for metric in metrics]\n",
    "\n",
    "print(\"Model Performance Comparison:\")\n",
    "print(metrics_df)\n",
    "\n",
    "# 分离训练时间和其他指标\n",
    "other_metrics = [\"Accuracy\", \"Precision\", \"Recall\", \"F1-score\"]\n",
    "training_time_metric = [\"Training Time\"]\n",
    "\n",
    "# 绘制其他四个指标的柱状图\n",
    "other_metrics_df = metrics_df[other_metrics].reset_index().melt(id_vars='index', var_name='Metric', value_name='Value')\n",
    "plt.figure(figsize=(12, 8))\n",
    "ax = sns.barplot(x='Metric', y='Value', hue='index', data=other_metrics_df, palette=[\"#007BFF\", \"#9370DB\"])\n",
    "\n",
    "# 添加数据标签，保留四位小数\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.annotate('{:.4f}'.format(height),\n",
    "                xy=(p.get_x() + p.get_width() / 2, height),\n",
    "                xytext=(0, 3),\n",
    "                textcoords=\"offset points\",\n",
    "                ha='center', va='bottom')\n",
    "\n",
    "plt.title(\"Model Performance Comparison - Other Metrics\", fontsize=16)\n",
    "plt.xlabel(\"Metrics\", fontsize=14)\n",
    "plt.ylabel(\"Values\", fontsize=14)\n",
    "# 横坐标标签保持平行\n",
    "plt.xticks(rotation=0, fontsize=12)\n",
    "plt.legend(title=\"Models\", fontsize=12, title_fontsize=14, loc='upper right')\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 绘制训练时间的柱状图\n",
    "training_time_df = metrics_df[training_time_metric].reset_index().melt(id_vars='index', var_name='Metric', value_name='Value')\n",
    "plt.figure(figsize=(6, 8))\n",
    "# 修改颜色为蓝紫色\n",
    "ax = sns.barplot(x='Metric', y='Value', hue='index', data=training_time_df, palette=[\"#007BFF\", \"#9370DB\"])\n",
    "\n",
    "# 添加数据标签，保留四位小数\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.annotate('{:.4f}'.format(height),\n",
    "                xy=(p.get_x() + p.get_width() / 2, height),\n",
    "                xytext=(0, 3),\n",
    "                textcoords=\"offset points\",\n",
    "                ha='center', va='bottom')\n",
    "\n",
    "plt.title(\"Model Performance Comparison - Training Time\", fontsize=16)\n",
    "plt.xlabel(\"Metrics\", fontsize=14)\n",
    "plt.ylabel(\"Values\", fontsize=14)\n",
    "plt.xticks(rotation=0, fontsize=12)\n",
    "plt.legend(title=\"Models\", fontsize=12, title_fontsize=14, loc='upper right')\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 讨论不同模型性能差异原因\n",
    "print(\"\\nModel Performance Discussion:\")\n",
    "print(\"Sklearn Perceptron:\")\n",
    "print(\" - It is optimized with advanced algorithms and numerical techniques, so it usually has high training efficiency.\")\n",
    "print(\" - It has been well - tested and tuned, which may lead to better accuracy in most cases.\")\n",
    "print(\"Custom Perceptron:\")\n",
    "print(\" - The implementation is relatively simple and lacks advanced optimization methods, resulting in lower training efficiency.\")\n",
    "print(\" - Due to the simplicity of the algorithm, it may not be able to fully capture the complex patterns in the data, leading to lower accuracy.\")\n",
    "print(\"Both perceptron models are suitable for linearly separable data. Their limitation is that they cannot handle non - linearly separable data well.\")"
   ],
   "id": "5212dd5930fef8bb",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance Comparison:\n",
      "                    Accuracy  Precision    Recall  F1-score  Training Time\n",
      "Sklearn Perceptron  0.881071   0.880965  0.881071  0.880776       8.723725\n",
      "Custom Perceptron   0.108643   0.022661  0.108643  0.037500     450.158968\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ],
      "image/png": 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KVq1aZfP19bU99dRTtpiYGIc6T5061ebr62t79NFHHcrtx65bt67tr7/+MsqPHTtmCw4Otvn6+trWr19vlK9evdrm6+tra9Cgge3ff/81yqOjo22PPfaYzdfX17ZmzRqbzWaz/fvvv7YqVarYAgICbD///LNDu8eNG5fi+k3LyZMnbb6+vrbKlSunuCfSw2q12tq0aWPz9fW1vf766w7X5m+//Wa0c926dUa5/bX39fW1ffnll0b5qVOnbNWrVzeum82bNxvLNm/ebPP19bUFBAQ41NN+jqtXr2775ZdfjPJDhw7ZGjZsaPP19bWtWLHCKH/77bdtvr6+trffftvhHo6Li7P16dPH5uvra3vttdeM8qNHjxp1ffPNN41y+/U1bNgwm6+vr23BggU2m81mu379uq169eq24OBg2+nTp431ExISbP369bP5+vraPv74Y6N81qxZxn19+PBho/zKlSu2Xr162Xx9fW39+vVLtT6dO3d2uG7XrVtnvJaXLl1K5dW6e9u3b7f5+vraGjVqlO5t5s+fb/P19bX16NHDKEv+XE4u+f389ttvG+fZarXa3nzzTZuvr6+tU6dODtt07tzZ5uvraxs1apQtPj7eKN+1a5etZs2atipVqtj27dtnlNtfs4CAANuRI0dsNtt/r+et2K+1Q4cOGfd88uvXrkOHDrb69evbkpKSbF26dEnxbPz555+N+/zPP/80yq9fv257+eWXUz0vyY+dWnmbNm1scXFxDm1J7Rzv3r3bVrFiRVuNGjVsUVFRRvm1a9eMus6YMcNms9lsr7zyis3X19c2b948h2Pan1NNmjS57TkDANzA8D0AgKQbQ/hCQ0MVHR2t7du3Oyxbvny5XFxc1LRp01S3XbFihU6fPq1atWqpX79+Dj11GjVqpJ49eyoxMdEYEhYfH69FixbJ1dVVo0aNUt68eY31n3rqKYWFhaU4xsKFC3Xp0iV17NhRjz/+uMOyDh066JFHHtHRo0fveq6nqKgo/e9//3P46du3r5o2baphw4bJYrHopZdeUpkyZSRJp0+f1tKlS1WkSBG9++67Dj1EypQpY3wb2+eff57iWAEBAQ7f9HfzXEXJHT9+XGvWrFH+/Pn14YcfOhznoYceMr4+fvr06Sm29fHxcRh2efNxnnvuOaM9khy+9WvQoEEqXLiw8bd9eE7yr19PSEhQaGiohgwZInd3d4d9d+zYUZLS/HasXr16OfTCePDBBxUaGirpv15pkvT1119LujG8qFy5ckZ54cKFNWjQIJUtW9Y4xuzZs5WQkKB+/fo59PpzcnLSwIED5evrqx07dqQ5rNTuzJkzkqSCBQummBcoPaKiorRnzx6VLl1ab7zxhtzc3Ixl1apVM66N1F4zX19fdenSxfjbx8dHtWrVknTjNahXr56xrF69esqfP79iYmJ09uzZFPvq06ePQkJCjL8feughY0iY/bxKNyapbtCggfr37+9wD+fJk0ft2rWTlPbr2L17d+P3tK7jK1euKDY2Vu7u7ipYsKBR7uLioiFDhujNN99Uo0aNjPJZs2ZJkt555x2VLl3aKM+fP7/GjBkjT09PrV69WocPH05xrOHDhztct6GhoSpZsqQSExN14MCBVOt3t+xDem/+Nr1bKVq0qKQbz5H0euCBBzRs2DDjPFssFj333HOSpL179xrr/fbbb4qKijKGALq6uhrLqlevrj59+ighIUGzZ89OcYywsDBjqPKtnkupad68uaSUQ/hOnDihXbt2pTovm539Ofnqq6+qUqVKRrmbm5tGjBihBx54QMuXL7+j89WpUyflyZPntm2ZP3++MfQyKCjIKPfw8NDQoUNVunRp45lgv8+KFSvmsI9HH31UI0aM0P/+978cPWE9AJiJUAoAYEjtw0R8fLzWrl2runXrOnzIS84+D5V9+5vZAxH7er///rtiYmLk7++f6ge41MKvbdu2SVKa3w7YsGFDh/Uy6ujRo1qyZInDz5YtW2S1WvXEE09o9uzZDh/At2/frqSkJFWrVs1h2IhdgwYN5OTkpF9//TXFfDa+vr7prpc9KKxXr16qE0eHhISoaNGiOnXqlENglJ7jVK9e3eHv5K9z8g+GkoxjX79+3Sh7/PHH9cknnzgEH/Hx8frrr7+0ZMkSSTeGwqU2n8/NQ5MkGUN+7HO42Gw2bd++XU5OTmrcuHGK9Zs2baoVK1YYr4t9zqvUrhWLxWJM1p/W/Gl29qGDqc1XlR72/Tdp0iTV+XMee+wxOTs767fffjMmt7e7+TWR/ntdbn5NpBuBkuT4utjdHOJKN0IaFxcX/frrr0b7+vfvr+nTpxv7km4ESVFRUcY8OTfXU5Ly5s2rhx56KEX5zR544AGVLVtWJ0+eVOvWrTVt2jQjRClTpow6deqkKlWqSJJOnjypY8eOqVChQqpdu3aKfXl6eqb5Orq7u8vPzy/FNjdfV5nNfn0nD39uxx6S2O7gG+SqVKmS4hj24cRXr141whD7fRAcHJxqGGMPAFO7D+7k2XSzUqVKqUqVKvr111+NEEe68Y8bNpstzaGwSUlJxjxXqd277u7uqlWrlsN66ZHettjPgz0UT65atWpas2aNXnrpJUkyAuIBAwbonXfe0U8//aS4uDhJUnh4eIpv6AQApI05pQAAhnr16snLy0tr1qzRiBEj5OTkpM2bN+vSpUupfrC1s3/wePDBB1Ndbv8Xd/skzfb10/p2udTm+rFPHt2vX79btiGtyZLTq02bNho9enS617fXa/369al+ELaLjY3VpUuXHAKfAgUKpPs4tzvH9mVnz57VmTNnHHqW3O44yXutSHLoJXPzfD03z1dld/XqVS1YsECbNm3SwYMHdfr0aVmtVof1U/vgnTwAsbMHOPYP1xcuXFBCQoIKFy6coidWauzXwO3m9bl5QvKb2XuxXL58WYmJibecmDk19tcsrbmrPDw8VLhwYZ09e1bnz5936HWR2mtmP5c3vybJl93M2dnZuP+Sy5MnjwoVKmQc2x7YHD16VF9//bV27typQ4cOGRPO2/ef2mt4J9+uN2HCBPXv31/79+/XmDFjNGbMGHl7e6tx48bq2LGjKleuLCl917v9vN7cO8zT0zPV82F//W4XAEVFRWnevHkpymvVquUwL97N7K9fWpP0p8b+TLy5x82t3OqekW7cN05OTsb1/eWXX95yovLUnpl38mxKTYsWLbRnzx6tWbNG4eHhkm6EUqVKlTLmorvZxYsXjcAwODj4lvu/3b2bXGrnKzX266hEiRK3XffZZ5/V/v379d133xnn183NTbVr19bjjz+uli1bZqh3JQDcjwilAAAGNzc3NWnSRIsWLVJkZKRq166t5cuXK0+ePA6T597M/iEvrQ/G9uX2f91Paz271N7M23shNG7cWPnz509z27QmSb9X7MFJ+fLlU+3Bciu3Ow93uo39PCcfJpae49xp2HKzffv26ZlnnlF0dLQeeOAB+fv7q3nz5qpUqZKCg4MdhmTdLD3n4E6/Mc2+/uOPP37L3gq3m7y5SJEi8vHx0enTp/X777+n2qsruR9//FEHDx5Uw4YNjQm/byet1+xuXxO7W7Xffmz7sZYuXaphw4YpMTFRDz30kEJCQlS+fHn5+/vLarUqIiLijo9xM/u3Tm7dulXr16/Xli1bdOjQIc2fP18LFizQ8OHDFR4efttnSvL63+n1fjtHjhwxevgl5+LicstQqkKFCnJzc9Phw4d1/vz5NHuWJmcfQnonz470ts/+bKpatarD8Nz07O9ue/k0b95cH374oVasWKHw8HAdPnxYe/bsUe/evdPcxn7furm5qVmzZrfcf3p65tmlty130iPS1dVV77//vl544QWtXr1amzZt0s6dO/XTTz/pp59+0jfffKMZM2akuDYBACkRSgEAHDz22GNatGiRVq5cqcDAQK1fv16NGjW6ZRBk72WR1nwzR48elfTfXCv2XgH2r/K+WfIhH8mPcejQIXXr1k1169ZNf4PuMXtvmkqVKmnMmDH37Di3O8fSf+e5SJEi96weqRk5cqSio6PVs2dPDR482OFD4KVLl+56/wULFpSrq6suXbqkuLg4hznIpBtD1hYuXKhy5cqpdu3a8vb21vHjxzVgwIA7+vB6M4vFotDQUM2dO1erVq26bSg1bdo0/frrrzp9+rReeeWV275m165d0/nz5+Xs7Jyit1pmSUhISDUgiY2N1fnz55U3b14VKlRI165d04gRIyTd+BbBm+d1u9u52pJzdnZW/fr1Vb9+fUk35hqaPXu2ZsyYoTFjxujpp5/O0uu9bdu2atu27R1v5+npqcaNG2vVqlX65ptvbhnASDeGRi5fvlyS9OSTT2aorrdifzbVq1dPgwYNyvT938qDDz6oatWq6ddff9XZs2eNdt6qx639Pk9MTNS7775reqBTtGhRHT9+XKdOnXL4hlK7efPmycfHx2EIcZkyZdSrVy/16tVLsbGx2rBhg9566y1FRUVpzZo1t2wvAOAGBjsDABzUrVtXBQsW1Nq1a7VhwwZdu3bNYZLs1Njn17h5Yls7+wcS+5CMKlWqqECBAvrzzz914sSJFOtv2LAhzWNs3Lgx1WOMHTtWbdu21TfffHPLumY2e722b9+e6lw1e/bs0WOPPab+/fvf0bwxNwsKCpLFYtGWLVt09erVFMt/+eUXnT9/XiVLlkzX8JPMZO/t0bt37xS9ErZs2WL8ntGJf11dXVWtWjUlJSUZcxsl98svv2jkyJHGpN23u1ZeeuklPf3001q/fv1tj921a1e5urpqzpw52r9/f5rrrVmzRjt27JCzs7PRm8Zej3Xr1qXaC2PlypWyWq2qWbPmPZ1/JrXzsH79elmtVtWrV08Wi0X79u3TtWvXVKFChVS/aMB+3u/mGo6MjFTz5s2N8MuuRIkSevnll+Xl5aWYmBhduXJFJUqU0IMPPqgLFy6kOufRlStXjDrZz3N28MILL8jV1VWffPKJ/v777zTXs1qteu2113T16lWFhYU5TN5/tz297OznZdOmTanee2vXrtUTTzyht956K1OOd7MWLVrIarVq9erVWrFihcqXL3/LIc5ubm4KCAiQ1WrVpk2bUiy32Wzq3r27OnbsqN27d2d6fe2Tm//0008plu3fv19vvPGGJk2aJKvVqm7duql+/frGPFLSjTmvmjdvrlatWkm6syGGAHA/I5QCADhwdXVVkyZNdPbsWU2YMEEeHh6pTi6dXPPmzeXt7a3IyEh98sknDh9cf/rpJ02fPl3Ozs7q1KmTcYzOnTsrKSlJL730kkPIsmbNGn333XcpjtGhQwd5eHjoq6++0rJlyxyW/fjjj5oxY4b27Nkjf3//u2j9nStVqpTCwsJ06tQp40OmXXR0tF599VUdPHhQxYoVu6sPm/bjXL16VUOHDlVMTIyx7OjRo3r99dclyeEb28xi74Wzbt06h/Lt27fr7bffNv5ObZLs9LK36/3333foYXf+/Hl9+OGHkmR8GOzataucnZ310UcfaevWrQ77mTdvnr7//nv99ddfac5tk1y5cuXUs2dPxcfHKzw8XOvWrUsRzKxatUovvfSSbDabevToobJly0q6EcJWrlxZhw8f1ttvv62EhARjmz/++EMffPCBQ9vulbFjxzp849yBAweMedOeeeYZSf+9hgcPHnRY12azae7cuVqwYIGk1CdST68KFSroyJEj+u677/Trr786LNuwYYMuX76skiVLGnWx1+311183ekVJN3qYDR06VFevXlXjxo1vOe+U2SpVqqT//e9/io2NVZcuXfT999+nCISOHTumF154QatWrZKPj4/eeecdh+X2b4pLLXy+EyEhIapcubL27NmjDz74wOH+O3z4sN555x3t27fvlkP77sZjjz0mi8Wir776Snv37k1XryH7lxW8/fbb+vPPP41yq9WqCRMmaOvWrTp69KjD0Fv7+bpy5cpd1Tc8PFwWi0VTpkxxCBSvXbumkSNHSrrxjHFycpKnp6fOnj2rcePGOQwvvnjxohFqpef5AgBg+B4AIBXNmzfXwoULdeDAAbVs2TLFcKmbubu766OPPlKvXr00YcIEfffdd6pYsaJOnz6tnTt3ytnZWa+99prDm/SIiAjt2LFD27ZtU5MmTVSrVi2dO3dOO3bsUGBgoHbu3OlwDB8fH73//vsaPHiwBg8erI8//tj4Jq8//vhDkvTKK6/c8bxOmeHtt9/W4cOHtWzZMm3ZskVVq1aVxWJRVFSUYmJiFBgYqIEDB971cUaOHKlDhw5p/fr1Cg0NVc2aNRUbG6vIyEjFx8erZcuWDt8MaJZnn31W7733noYNG6b58+eraNGiOnLkiP766y8VLFhQRYsW1dmzZ3X27NlbDgO9lRYtWmjbtm2aN2+eWrRoYXyj2K+//qorV66oQ4cOxrxn/v7+evXVV/XOO++oe/fuqly5skqWLKmDBw9q3759cnZ21ocffpjuYV8DBgyQ1WrVp59+qoiICJUoUUK+vr5yc3PTn3/+aQwx69atm8PrbLFYNG7cOD3zzDOaN2+eNmzYoOrVq+vixYuKiopSUlKSevXqleq3TWYmZ2dnPfnkk6pTp45sNpt++eUXxcfHKyIiwvjGxNKlSys0NFTr169X69atFRwcrDx58hi9GcuXL69///3XmJg7IwoVKqShQ4fqvffeU3h4uAICAuTt7a3Tp09r165dcnFxcehF1bVrV+3cuVMrVqzQ448/rlq1asnd3V1RUVG6cOGCKlasqHffffeuz09m6969uwoVKqQRI0bopZde0vvvv6+qVasqT548OnHihPbs2SOr1apatWpp7NixKYZW2oecbtiwQb1791ZgYKD69Olzx/VIfv3NmDFDy5YtU5UqVRQXF6eoqCglJCSoadOm9ywULV68uAICAoxneXpCqSZNmui5557TF198oaeeekpVqlSRt7e3/v77bx09elTu7u6aOHGiw9C+hx56SAcOHNCAAQNUpUoVDR06NNXJ/W8nICBAgwcP1tixY9WuXTvjevvtt98UHR2thg0bqlu3bpKkYcOGKSoqSrNmzdLatWtVqVIlxcfHa8eOHbp69apatmx528naAQA3EEoBAFKoXbu2ChUqpAsXLqR7TowaNWpo8eLF+uyzz7Rp0yatW7dOhQoVUosWLfTss8+m+FdjNzc3TZ8+XTNnztTixYu1ceNGFS1aVEOGDFFgYGCqH5SaNm2qb7/9VtOnT9cvv/yiDRs26IEHHtAjjzyi5557zviAbbYHHnhACxYs0KxZs7RixQpt375dbm5uevjhh9WqVSt17NjxtsFeeo8zf/58zZw5UytWrNBPP/0kd3d3BQYGqmPHjrcdZnmvdO/eXUWLFtXMmTO1b98+7du3T8WLF1fXrl3Vs2dPTZs2TV9++aV+/PHHVOdqSa+33npLNWvW1Ny5cxUVFaXExESVK1dOTz/9tNELz65Lly6qVKmSZsyYoV9//VX79u2Tt7e3mjdvrl69ehnf8pZegwYNUqNGjfTNN99ox44d2rp1q6xWq4oUKaInnnhCnTt3Nob/JPfwww9r8eLFmjZtmtatW6f169fLy8tLDRo0ULdu3VSvXr0Mn4/0mjRpkhYuXKjVq1crPj5e1atX17PPPptimN748eP1+eefa9myZdq+fbvy58+vBx98UJ06ddIzzzyj9u3b659//tEff/yR4R6J3bt3l7e3t+bOnau///5bv//+uwoVKqTHH39cPXv2dAiVnZycNH78eDVs2NA479KNEKJHjx7q2rWr0Usmu3nyyScVEhKir7/+Wps2bVJkZKSSkpJUvHhxNWvWTG3atFGDBg1SHbZZpUoVDRkyRF9++aW2bNmi2NjYDIVS0o3r77vvvtP06dO1bt06bdmyRfny5ZO/v7+efvpptWrV6p5+S1zz5s21c+dOValSJd3zuw0bNky1atXSnDlz9Pvvv+uvv/5S8eLF1b59e/Xq1SvFfl599VVdu3ZNu3fv1s8//6wDBw5kKJSSpF69ehnPjd9//12xsbEqVaqUunXrpueee854vUqXLq158+bp008/1bZt27RhwwblzZtXFSpUULt27dSuXbsMHR8A7kcW291MDgAAAIBsKTQ0VMePH9fq1avvasJ3AACAe4U5pQAAAAAAAGA6QikAAAAAAACYjlAKAAAAAAAApmNOKQAAAAAAAJiOnlIAAAAAAAAwHaEUAAAAAAAATOeS1RXIKlarVYmJiXJycpLFYsnq6gAAAAAAAOQKNptNVqtVLi4ucnJKuz/UfRtKJSYm6vfff8/qagAAAAAAAORKVatWlZubW5rL79tQyp7UVa1aVc7OzllcGwAAAAAAgNwhKSlJv//++y17SUn3cShlH7Ln7OxMKAUAAAAAAJDJbjddEhOdAwAAAAAAwHSEUgAAAAAAADAdoRQAAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHT37bfvAQAAAACQmqSkJCUkJGR1NYBsx8XFRc7Ozrf9Vr107y9T9gIAAAAAQA5ns9l06tQpXbx4MaurAmRbzs7O8vb2VoECBe46nCKUAgAAAABAMgIpb29veXh4ZFpvECA3sNlsSkxM1OXLl3Xy5EnFxsaqePHid7VPQikAAAAAwH0vKSnJCKQeeOCBrK4OkG15enoqT548OnfunLy9veXs7JzhfTHROQAAAADgvmefQ8rDwyOLawJkf/ny5ZPNZrvrudcIpQAAAAAA+H8M2QNuL7PuE0IpAAAAAAAAmI5QCgAAAAAA3NakSZPk5+cnPz8/zZkz55brhoaGys/PT127ds2042/btk1+fn6aNGlShrbP7Prg7hFKAQAAAACAO7Jy5co0l+3atUvHjx83sTbIqQil7mPR0dGKiIhQzZo1FRISolGjRikxMTHVdWfNmqXQ0FDVqFFDLVu21KpVq4xlcXFxGjFihOrVq6datWrpmWee0d9//51iH7GxserQoYMWLVqUYtnhw4cVEhKiY8eOZV4DAQBArsJ7FwDIHh566CFFRUXp3LlzqS5fvnw532CIdCGUuo8NHDhQHh4e2rRpkxYuXKitW7dq5syZKdbbuHGjpk6dqunTp2vHjh3q16+fBg4caLwJmzRpkg4dOqRly5Zpy5Ytqlixovr16+ewj3379ik8PFy7du1Ksf9169apU6dOunjx4j1oJQCzZZcPjQcPHtQzzzyjwMBA1a9fX59++mnmNhSA6XjvAuBesL93adeunU6ePKkzZ87IZrOluu65c+e0d+9e/fnnn9q3b58uXbpkLLNarTp+/Lj+/vtv/fnnnzp48KBiY2ON5UlJSTp27Jj++usv/fnnnzp27JiSkpKM5VevXtW///6rP//8U3v37r1lPbJa8+bNZbVatXr16hTLbDabVq5cqcceeywLaoachlDqPnX48GFFRkZq6NChcnd3V6lSpRQREZHquOADBw7IZrMZP87OznJ1dZWLi4skaf/+/cYySXJycpK7u7ux/datW/XMM8+oTZs2KlGihMO+J0+erHHjxmnQoEH3sLUAzJQdPjQmJCSoT58+qlq1qrZt26bPPvtMc+bM0YoVK+5VswHcY7x3AXCv2N+7zJkzR0WLFlVMTEyqPYCuXLmis2fPqkyZMqpcubK8vb119OhRxcfHS5LOnDmj+Ph4lS9fXhUrVlTevHl15MgRY/uTJ08qISFBFSpUkK+vr+Lj43X69GlJUmJiog4fPixvb29VrlxZDz30kKKjo7Nt+F27dm0VLlw41SF8UVFROn36tB5//PEUyy5duqTRo0crLCxM/v7+qlOnjgYPHqz9+/enWPevv/5Snz59VKtWLdWsWVMvvfSSoqOjU63PgQMHNHjwYNWpU0f+/v5q2rSpJkyYoLi4uFu2IykpSZMnT1bLli0VEBCgmjVrqmvXrlq/fn06zwTulktWVwBZY9++fSpYsKB8fHyMsnLlyunEiRO6fPmyvLy8jPLHH39cixYtUosWLeTs7CyLxaIPP/xQxYoVkyQ999xzevHFF1W7dm05OzurUKFCmj17trF9xYoV9eOPPypPnjyaMWOGQz2eeuop9e3bl/HGQC5h/9D4008/OXxo/PDDD9WjRw+HdTPjQ+OQIUP0wgsv6MKFCw773r59u86cOaP+/fvLzc1NlStXVteuXTVnzhw1b978Hp8FAPcC710A3AvJ37vkzZtXLi4ueuCBB3T+/HkVLVrUYd3r169LkvH+xGKxGD/25Tf3bHJyutEPxGq16tKlSypTpozxXqdYsWI6ePCgihUrpoSEBIf3RTdvn904OTnp0Ucf1cKFC3Xu3DkVKVLEWLZs2TIVL15cNWrUcNjm3Llz6tSpk44eParWrVurWrVqOnbsmObOnav169dr+vTpqlmzpiRpz5496tKli9zc3NStWzd5enrqhx9+0Nq1a1PUZffu3erevbvy58+v8PBwFS5cWLt27dKnn36qrVu3avbs2cqTJ0+q7Xjvvfc0Z84cPf300+rWrZuuXLmiefPmKSIiQlOnTlWjRo0y8awhNYRS96lr1645fLiTZPwdExPj8MYuISFBFStW1KhRo1SxYkUtWbJEr732msqVKyc/Pz8lJSWpWbNm6tu3r/Lly6cPPvhAERER+uGHH5QnTx4VKlQozXokf2MJIOfLLh8a9+3bp4cfflhubm5GWfny5fXZZ5/dq6YDuMd47wLgXkj+3sXeq8bNzU0JCQlKSkqSs7OzsW6BAgV04cIF/fvvv0ZZqVKl5OrqKkkqUqSIjhw5Ykw34OLiojJlykj6L7DKmzevsW2ePHlks9l0/fp15c2bV15eXjp69KixvHDhwipQoMA9a/vdatGihebPn6/Vq1erc+fOkm70PFq9erVat25thHV248aN05EjR/Tuu++qXbt2RnmbNm3Upk0bvfrqq1qxYoWcnZ01evRoxcfH65tvvlH58uUlSZ07d1b37t3166+/GtvabDa9+uqr8vLy0nfffaeCBQsa69aqVUuvv/66Zs+erZ49e6bahm+//Vb169fXW2+95dCurl276vfffyeUMkH2jF1xz3l4eDiMb5Zk/J0vXz6H8rffflsVKlRQtWrV5Obmpnbt2ikgIECLFy9WQkKCBgwYoLZt28rHx0f58+fX8OHDdfr0aW3ZssW09gDIHm73oTE5+4fGb775Rrt27dLIkSP12muvae/evZJkfGj86aefFBkZqbCwMEVERBj/SlmoUKE0/9UrrXrcXAcAOQfvXQDcC6m9Z7CHKVar1aHcHiqVLVtWlStX1oMPPqjjx48bYZbNZpOXl5f8/PxUqVIleXp66siRI7Jarca+kvd8St6Lyt5rvFSpUqpcubIefvhhXbp0SefPn79nbb9bwcHBKlKkiMMQvl9++UXR0dEphu7Z55966KGH1LZtW4dlFSpU0JNPPqnDhw9rz549unDhgqKiotSgQQMjkJJuhIXPPvusw7Z79+7Vvn371KhRI1mtVp0/f974ady4sfLkyaM1a9ak2YZixYopMjJSn3/+uTGFRLFixbRmzZoU00bg3iCUuk9VqFBBFy9edBgrvX//fhUrVkyenp4O6544ccIYJ23n4uIiV1dXxcTE6NKlSw7L7T0e7P9iAOD+kV0+NKZVj5vrACDn4L0LgHshtfcMyacOSO7kyZPKmzevPDw85OTkpEKFCsnd3V0XLlyQ1WrV0aNHVahQIbm6usrZ2VnFixdXQkKCrl696hBA2SUPqs6fP6+EhAQVKFBATk5OypcvnzGMMLtycnJSs2bNFBUVZcz1tHz5cpUpU0ZVqlRxWPfChQu6cuWKypUrl6IHlXTjGS9Jx44d07Fjx2S1WvXQQw+lWC95SCXd+GIbSZo3b57q1Knj8FOvXj1dv379lsOtR40apQIFCuiDDz5QWFiYHn30Ub355pvavn37nZ0MZBih1H2qTJkyCgoK0rvvvqurV6/q6NGjmjJlitq3b59i3dDQUH311Vfas2ePrFarVq5cqW3btqlFixYqUKCAgoKCNGbMGEVHR+v69ev68MMPVahQIQUFBWVBywBkpezyobFChQo6dOiQw7f+/fvvv8YbHgA5D+9dANwLqb13iY+Pl4uLi8PQPXv5zXNG2eeUslqtSkpKcliefM6pPHnyyGKxGD2+pRtD+uzL4uPjU/TMSj5fVXbVvHlzY8heQkKC1q5dm+oE5/bzklZ77G1PPvVCat88ePM5sv8dHh6uGTNmpPozfvz4NOtfo0YNrV27VtOmTVO3bt3k7u6uefPmqUuXLho9evRtWo/MQCh1H5s4caISExMVFhamp59+Wg0aNFBERIQkKTAwUD/88IMkqV+/fgoPD9eLL76oWrVq6bPPPtPHH3+sSpUqGfspU6aMWrVqpYYNG2r//v36/PPP5eHhkWVtA5A1ssuHxpCQEBUqVEhjx47V9evX9ffff+vLL79MtR4Acg7euwDIbMnfu1y7dk2JiYmKjo5OdW45Ly8vRUdHKzY2VjabTZcuXdK1a9dUoEABubi4yMPDQ6dOnVJiYqKsVqtOnTolZ2dno2eVl5eXsTwxMVGnTp0yekZ5eXkpJiZGFy5ckM1mU2xsrKKjo405krKroKAgeXt7a8WKFdqyZYsuXryYaihVuHBh5c+fX//++2+qYdO+ffskScWLF1epUqXk5OSU6jfyHT582OHvkiVLGr/XrVvX4ad27dq6dOlSms/269eva/fu3Tp58qQaNmyo1157TT/88IPWrFmj0qVLa9asWbp69eodnQ/cOSY6v48VKVJEEydOTHXZzp07jd9dXFz04osv6sUXX0xzPx988EG6jpnWV2uWLFnSmEcGQM42ceJEjRw5UmFhYXJyclLr1q0dPjS+9dZbatWqlfr16ydnZ2e9+OKLunTpkh566KEUHxo/+OADtWrVSomJiapevXq6PzS6uLjoiy++0MiRI1WvXj15eHioa9euKeYwAJCz8N4FwL1gf+/y7LPPatCgQSpXrpy8vb0lSX/++adKlCihggULGmVHjhxRUlKS3NzcVLp0aWNOqlKlSun06dNG8OLu7q4yZcoYPa5KlCihU6dOGcs9PT1VvHhxSVL+/PlVqlQpnTlzRidPnpSLi4uKFCmiwoULZ8EZST/7EL6vv/5a7u7uqlixosqVK5fqeo8++qgWL16sRYsWOUx0vn//fi1ZssSYT8tisahu3brasmWLdu3apYCAAEk35hu9+ctt/P399eCDD2rx4sXq3r27SpcubSybP3++3nzzTXXu3Fn+/v4p6nThwgV16NBB9erV0/Tp043yUqVKycfHR8ePH8+2336Ym1hsqcWU94GkpCTjAr+5WyYAAAAA4P4SFxengwcP6uGHH3b4ljz8Z9KkSZo8ebJmz56tkJAQSdKOHTvUqVMnSdKQIUPUq1cvY30/Pz8FBwfryy+/1NmzZ9WhQwedOHFCrVu3VvXq1XXs2DHNmzdPiYmJmjZtmoKDgyXdmCuqY8eOio+PV5cuXYzeWIcOHVJ0dLT69etn/MPDzz//rN69eytPnjzq2LGjSpcurd9//13ffvutHnzwQX399dcqWrRoivpI0ogRIzR//nzVr19foaGhslgs2rx5s9atW6cuXbpo+PDhpp3bnOZ290t6Mxd6SgEAAAAAgAwJDAxU8eLFdfLkSbVo0SLN9YoWLaqFCxdqypQpWr9+vZYuXaqCBQsqNDRUffr0cehh9fDDD2vBggUaP368FixYoPj4eNWtW1cDBgxQt27dHPZbt25dLViwQJ988om+/fZbXblyRcWKFVPnzp3Vu3dvI5BKzYgRI1S2bFktXrxY48aNU1JSksqWLavhw4erc+fOd39ycFv0lKKnFAAAAADc9+gpBaRfZvWUYoAkAAAAAAAATEcoBQAAAAAAANMRSgEAAAAAAMB0hFIAAAAAAAAwHaGUiZKs9+Wc8rkCrx2yM67PnI3XD9kV12bOxuuH7IzrM+e6T78nDfeQS1ZX4H7i7GTRwG/O6t+zCVldFdyB8kVdNeGptL9GFMhqPFtyLp4vyM54tuRcPFuQ3WXX50uRvIl61j9Jluh4ObvSf+NmeVwsKl3YNaurgVyGUMpk/55N0J6T8VldDQC5DM8WAPcCzxYA90p2fL6UyJ+khCSbridKFtEjCDAD8S8AAAAAAABMRygFAAAAAAAA0xFKAQAAAAAAwHSEUgAAAAAAADAdE50DAAAAAHAbrs6Si5MlS+uQaLUpISlLq4AsZrPZZLFk7XWYmQilAAAAAAC4BVdnqUJRN7k4Z3EolWTTvrPxGQqmjhzcp2+/mqY9u7br6pVL8vQqqErVgtSm03N6uEIlY703Bj0nSXpr/Bep7mfRokV65ZVXtG7dOpUsWTJD7TDDpEmTNHnyZIcyZ2dneXp6qmbNmho4cKAqVKiQRbXLmHXr1mnVqlX64IMPsroqmYZQCgAAAACAW3BxssjF2aKB35zVv2cTsqQO5Yu6asJTReXiZFFCku2Otj168F+91q+rylf013MvvqwChR7Q+bOntWLx13qtX1e9Of5z+Vaufo9qnrXmz59v/J6UlKQTJ05o/PjxCg8P17Jly1S0aNEsrN2dmTlzZlZXIdMRSgEAAAAAkA7/nk3QnpPxWV2NO7Zk4ZfK71lAr73/iVxcXI3yWvUaa2D3J7Xwy8/06nsfZ2EN752AgACHv4OCglS8eHGFh4dr8eLF6tWrV9ZUDJKY6BwAAAAAgFzt4vlzkiTbTR2s8rp76JmIoar7SNM0t921fYs6NQvShHdfl+3mHfy/qKgodenSRdWrV1dwcLCGDRum8+fPO6yzfft2Pf/886pVq5b8/f0VGhqqSZMmyWq1SpKOHTsmPz8/zZgxQ82bN1dwcLAWLVqkSZMm6dFHH9WGDRvUsmVL+fv7q1mzZlq8eHGGz4e/v78k6fjx40bZP//8o969e6tGjRqqUaOG+vbtq6NHjxrLt23bJj8/P82bN0+NGzdW3bp1tXnzZknSli1bFB4ersDAQNWvX18jRozQpUuXjG1PnDihwYMHKzg4WNWrV9czzzyjP//801hub/uyZcvUp08fVa9eXY0aNXI4P127dlVkZKQiIyPl5+enbdu23bZOnTt3VlBQkEJCQjRkyBCdPHnSOOaiRYtUuXJl/fbbb+rQoYOqVq2qRx55RNOmTcvwec0IQikAAAAAAHKxoNoNde7MSb3Wr4tWfjdPxw4fMAKmOo2a6pFmT6a63Z+/RenDEYNUr/Fj6v/yyFQn2N6+fbu6d++uvHnzasKECXr11VcVGRmpbt26KS4uTpL0999/q3v37ipYsKDGjx+vTz75RDVq1NDkyZO1bNkyh/2NHz9ezz//vN555x3Vrl1bknT27FmNHDlS3bp102effaaSJUvq5Zdf1v79+zN0Pg4ePChJKl26tPF3x44dFR0drdGjR2vUqFE6evSoOnXqpOjo6BT1GzZsmIYNG6aAgABt3LhRPXr0MNo2dOhQrV+/Xv3795cknT9/Xh07dtSePXs0fPhwjR07VlarVeHh4Snq/+abbyp//vyaNGmSWrdurSlTphjzR73xxhuqXLmyKleurPnz56tKlSpp1un777/Xc889Jx8fH40bN06vvPKKdu7cqQ4dOji0x2q1auDAgWrRooU+++wzBQUFacyYMdq0aVOGzmtGMHwPAAAAAIBcrNmTHXTh/Dn9MH+mPp/4riTJs0AhBdSqq+ZtOqlCpWoptvn37z80+rUXFdKgiSJeeltOTqn3aRk7dqwefvhhTZ06Vc7OzpKk6tWr6/HHH9e3336r8PBw/f3336pbt64+/PBDYz/16tXThg0btH37drVs2dLYX9OmTdW+fXuHY8TGxmrUqFGqU6eOJKlMmTJq3LixNm7cqHLlyt2y7YmJicbvcXFx+vvvv/Xuu+/K09NTrVq1kiRNnjxZefPm1cyZM5U/f35JUp06ddSkSRNNnz5dw4YNM/bRsWNHPfbYY8bfEydOVMWKFfXxx/8Nf8ybN6/GjRun06dP6+uvv9bFixc1d+5cPfjgg5Kkhg0bqkWLFvroo480ceJEY7vKlStrzJgxxjoxMTH66quvFBERofLlyxt1u3lIYvI6Wa1Wffjhh6pbt67Gjx9vrFOjRg21aNFCX3zxhYYOHSrpxjf5RURE6KmnnpJ0Y2jjmjVrtGHDBjVo0OCW5zWz0FMKAAAAAIBcruOzffXZN+s04LXRCm3RRh4e+bRp7TK91q+rln37lcO6586c1KiXX5DValXPga+lGUjFxsbqt99+U6NGjWSz2ZSYmKjExESVKlVK5cqV05YtWyRJrVu31rRp05SQkKB9+/Zp7dq1mjRpkpKSkpSQ4DhxvK+vb6rHSh7EFCtWTJIUExNz23ZXqVLF+AkKClJ4eLiuX7+uSZMmGZOc//LLLwoJCVHevHmNNuTPn181a9bUzz//7LA/Pz8/4/e4uDjt2bNHTZo0cVinWbNmWrVqlXx8fLR161ZVqlRJPj4+xr6dnJzUsGHDFPu2h2TJ95OQkKBdu3bdso3J63Tw4EGdPXvWIeiTbvQKCwwM1LZt2xzKAwMDjd/d3NxUuHDhdJ3XzEJPKQAAAAAA7gP5Pb1UP6yF6oe1kCQd3PeXJr33quZ8NkENmzwhzwIFJUlnTh5X9Zp1tee37Zo/c4q6RwxNdX+XL1+W1WrVtGnTUp2LKE+ePJJuhDdvv/22vv/+eyUmJqpkyZIKDAyUi4tLinmqihQpkuqx3N3djd/tIVlac1wlt3DhQuN3V1dXFS1aVA888IDDOhcvXtTy5cu1fPnyFNsXLlzY4e/k2166dEk2my3F/m7e9+HDhx2G2yUXGxtr/O7t7Z3qsS9fvpzm/m+u08WLFyWlfh6LFCniMJeVdKNXV3JOTk7pOq+ZhVAKAAAAAIBcKvrsab0S0Vkdnu2rsBZtHZY9XKGSOj73oj4cMVCnThw1QqlSZcrplfcma/7MKfp+7gzVD22uqlWrpth3vnz5ZLFY1L17dz3++OMpltuDpFGjRmnVqlWaMGGC6tatKw8PD0kyhuPdS6nV+2aenp6qW7eunn322RTLXFzSjk3y588vi8WSYlL3+Ph4bd26VdWqVZOnp6eCg4P10ksvpboPNzc343d7oGRnn//pVqHXzQoWLChJOnfuXIplZ8+eVaFChdK9LzMwfA8AAAAAgFyqYOEicnJ21qrv5ik+/nqK5SeOHpKrWx4VL/mQUeZZoJCcnV3UrksvFfUprk/GvKHExIQU2+bPn1+VK1fWgQMHVLVqVeOnQoUKmjx5sjFU7Ndff1VISIiaNGliBFJ//PGHzp8/b3y7XFYKDg7Wv//+q0qVKhlt8Pf318yZM7VmzZo0t8uXL58qVaqkdevWOZRv3rxZvXr10qlTpxQcHKyDBw/q4YcfdjhHP/zwg7755htjHi5JWr9+vcN+Vq1aJXd3d1WvXl2S0hxGmdzDDz+sokWLasmSJQ7lR48e1a5du1SjRo3b7sNMhFIAAAAAAORSzs7O6jnwdR05+K9e7tNRq39YoD2/RWnntk2a+fEHmvfFZD39zAvK7+mVYts8efLq+f6v6MiBfVo454tU9z948GBt3rxZQ4YM0caNG7V+/Xr16NFDP//8szFkrVq1atq8ebPmzp2ryMhIzZ49Wz179pTFYnEYvpZVIiIidOTIEfXu3Vtr167Vpk2b9OKLL2rZsmWqWLHiLbft37+/9uzZo4EDB+qnn37Sd999pzfeeEONGzdWpUqV1L17d1mtVnXv3l3Lly/X1q1bNXz4cM2ePVtly5Z12NfKlSs1atQobd68WePGjdOcOXMUERFhBHleXl46ePCgtm7dqkuXLqVaHycnJw0ePFg///yzBg0apI0bN+q7777Ts88+qwIFCqTaGywrMXwPAAAAAIB0KF/UNUceO6h2Q7378Vf6Yf5MLZozTZcvXpCLm5vKVqikQcM/UEjDJmluGxjSQLUbPqr5sz9T57YtUiyvX7++Pv/8c02ePFn9+/eXq6urqlSpohkzZhiTk7/88stKSEjQhAkTFB8fr5IlS+qFF17Qv//+q/Xr1yspKSnDbcsMFStW1Jw5czR+/Hi99NJLstls8vX11ccff6ywsLBbbtu4cWNNnTpVkyZNUt++fVWoUCE1b95cAwYMkCT5+Pho3rx5Gjt2rN58801dv35dZcqU0ahRo1J8y+CAAQMUGRmp+fPnq3jx4hoxYoQ6depkLA8PD9cff/yhnj176r333ksxB5Vd27ZtlS9fPk2dOlV9+/ZV/vz51aBBAw0ePNiY3D27sNjMnMEqG0lKStKuXbsUEBDg0F3uXntiygntORlv2vFw96oUd9PSiBJZXQ3glni25Ew8X5Dd8WzJmXi2ICfIjs+XEvmT9FbDGBUt8ZAsLnkclrk6SxWKusnF2ZJFtbshMcmmfWfjlZAFGY67q0UVvN1uvyLu2LFjxxQWFqb33ntPbdu2vf0G2UBcXJwxLPHmydKl9Gcu9JQCAAAAAOAWEpKkfWfj5eKUxaGU1ZYlgRRwrxBKAQAAAABwGwlJUkLSfTnQCLhnCKUAAAAAAACySMmSJbV3796srkaW4Nv3AAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAAACmI5QCAAAAAACA6QilAAAAAAAAYDpCKQAAAAAAAJiOUAoAAAAAANwTNpstq6uQ6+Smc+qS1RUAAAAAACC7c3WWXJwsWVqHRKtNCUkZ337/3j1a9u0c/bk7SpcvXlChB4qoamCI2nTuIZ8SJTOvov/vk08+kaurq3r06JHp+06PSZMmafLkyQ5lzs7O8vT0VM2aNTVw4EBVqFAhS+qWUevWrdOqVav0wQcfZHVVMgWhFAAAAAAAt+DqLPkWdZOzc9aGUklJNv1zNj5DwdTK7+Zp5scfqEpALYX3HKDCD3jr1PEj+n7+TP2yaa1GfPiZyvpWztT6TpgwQf369cvUfWbE/Pnzjd+TkpJ04sQJjR8/XuHh4Vq2bJmKFi2ahbW7MzNnzszqKmQqQikAAAAAAG7BxckiZ2eL1i08pItn47KkDgWL5lVY+zJycbIoIenOhm/9/cdOzZj8vh5r3VHP9htmlFcJqKVa9UP1Uu8OmvLBCI2ZvjCzq50tBAQEOPwdFBSk4sWLKzw8XIsXL1avXr2ypmJgTikAAAAAANLj4tk4nTsZmyU/dxOG/TB/pvLl91TnHv1TLPMqUEjPvPA/hTRsotiYa5KkNwY9pzcGPeew3u6d2+Xn56dt27ZJkqxWqz766COFhobK399foaGhGjdunBISEiRJfn5+kqTJkycbv0vS77//rueff14hISGqUaOG+vTpo3379hnLt23bJj8/P23dulVdu3ZVtWrV9Mgjj+ibb77RmTNn1K9fPwUGBqpRo0Z31WvI399fknT8+HGj7J9//lHv3r1Vo0YN1ahRQ3379tXRo0dT1G3evHlq3Lix6tatq82bN0uStmzZovDwcAUGBqp+/foaMWKELl26ZGx74sQJDR48WMHBwapevbqeeeYZ/fnnn8byY8eOyc/PT8uWLVOfPn1UvXp1NWrUSJMmTZLVapUkde3aVZGRkYqMjDRei9vVqXPnzgoKClJISIiGDBmikydPGsdctGiRKleurN9++00dOnRQ1apV9cgjj2jatGkZPq93ilAKAAAAAIBcymazadf2n+VfI0R58rqnuk6dRk31VLc+cvfIl+79Tps2TXPmzFHfvn31xRdfqFOnTpo+fbo+/fRTSf8NmWvfvr3x+y+//KJOnTrJarVq1KhReuedd3Ty5El17NhR+/fvd9j/4MGDFRoaqk8//VRlypTRG2+8oW7dusnX11cTJ05UlSpV9N5772n37t0ZOS06ePCgJKl06dLG3x07dlR0dLRGjx6tUaNG6ejRo+rUqZOio6Mdth0/fryGDRumYcOGKSAgQBs3blSPHj1UsGBBjR8/XkOHDtX69evVv/+NEPD8+fPq2LGj9uzZo+HDh2vs2LGyWq0KDw9P0e4333xT+fPn16RJk9S6dWt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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x800 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Performance Discussion:\n",
      "Sklearn Perceptron:\n",
      " - It is optimized with advanced algorithms and numerical techniques, so it usually has high training efficiency.\n",
      " - It has been well - tested and tuned, which may lead to better accuracy in most cases.\n",
      "Custom Perceptron:\n",
      " - The implementation is relatively simple and lacks advanced optimization methods, resulting in lower training efficiency.\n",
      " - Due to the simplicity of the algorithm, it may not be able to fully capture the complex patterns in the data, leading to lower accuracy.\n",
      "Both perceptron models are suitable for linearly separable data. Their limitation is that they cannot handle non - linearly separable data well.\n"
     ]
    }
   ],
   "execution_count": 31
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
